Computational Scientist – Molecular Modeling & Simulation Full-time NEW

Job Title

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it's work worth doing. If you're driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.

At Eli Lilly and Company, we unite caring with discovery to make life better for people around the world. Lilly is a leader in global healthcare and has been discovering and developing medicines that help improve lives for more than 140 years. Our 50,000 employees around the world work as a team to bring breakthrough medicines to patients who need them. We are looking for motivated, highly skilled scientists and engineers to help us continue to bring breakthrough medicines to patients.

Delivery, Devices, and Connected Solutions (DDCS) sits within Lilly's Product Research & Development organization. We are a diverse team of scientists and engineers responsible for discovering, designing, and developing patient-centric drug delivery solutions across a broad range of modalities — from injection devices to novel routes of administration and nanomedicines. DDCS drives the drug delivery innovation agenda across early and late development to meet the needs of an expanding portfolio that spans small molecules, biologics, and nucleic acid therapeutics.

Computational Modeling & Simulation (M&S) is a disciplinary horizontal within DDCS that builds and applies predictive models spanning molecular to system scales. M&S outputs inform device architecture, formulation, and delivery-system design, and development strategy — reducing empirical iteration, quantifying risk, and building mechanistic understanding of how our delivery systems perform in real use. The scope of the capability spans continuum-scale multiphysics (CFD and FEA), molecular and mesoscale simulation, and scientific machine learning, working closely with Formulation Sciences, Device Engineering, Analytical Sciences, Manufacturing, Quality, and Regulatory.

This role builds and applies molecular-scale modeling within DDCS. You will independently design and execute simulations that explain and predict how drug molecules, excipients, and delivery vehicles behave — spanning all-atom molecular dynamics, coarse-grained and mesoscale models of self-assembly and interfacial behavior, and the first-principles calculations needed to parameterize force fields for chemistries that existing parameter sets do not cover.

The role sits within the Computational Modeling & Simulation team in DDCS and works across the programs that the team supports. You will work independently on method selection, execution, interpretation, and technical defense of results while collaborating with stakeholders from across the department. You will also serve as the technical point of contact between DDCS and Lilly's broader computational chemistry community, so that molecular-scale work across the company is coordinated and reused rather than duplicated.

Molecular and mesoscale modeling in DDCS resolves the phenomena that govern formulation stability, the assembly and structure of delivery vehicles, and molecular-scale transport. Focus areas include:

• Biologic and peptide formulations
• Lipid- and polymer-based nanocarriers
• Interfacial behavior
• Small-molecule and peptide physiochemistry
• Materials and container-closure interactions
• Property hand-off to higher length scales

Key Responsibilities:

• Molecular and Mesoscale Simulation
• Independently design, execute, and interpret all-atom molecular dynamics, coarse-grained, and mesoscale (e.g., dissipative particle dynamics) simulations to answer defined formulation and delivery questions across early- and late-phase programs.
• Apply enhanced-sampling and free-energy methods — umbrella sampling, metadynamics, free-energy perturbation, thermodynamic integration — where unbiased equilibrium sampling cannot reach the relevant states.
• Assign and justify protonation states for titratable groups as a routine condition of simulation setup, and apply constant-pH molecular dynamics or continuum electrostatics where charge state is itself the question — for example, ionizable-lipid behavior in assembled bilayers.
• Compute thermophysical and rheological properties from simulation and report them with statistical uncertainty and an explicit domain of validity in concentration, temperature, and shear regime.
• Force Fields, Parameterization, and First-Principles Support
• Apply electronic-structure and semi-empirical methods (e.g., DFT, xTB) to derive partial charges, torsional parameters, and conformer energetics where empirical force-field parameters are missing or unreliable for a given chemistry.
• Parameterize and validate force fields for novel excipients, lipids, and polymer chemistries, including systematic coarse-graining where all-atom representations are computationally prohibitive.
• Assess reaction and degradation energetics relevant to formulation and container-closure stability, in partnership with Lilly's computational chemistry groups rather than in parallel with them.
• Multiscale Property Hand-Off
• Translate molecular-scale outputs into constitutive inputs for continuum injection, device, and process models, defining in advance the accuracy and uncertainty the decision actually requires.
• Establish and communicate where molecular-scale prediction is fit for purpose and where it is not — including recommending experiment over simulation when that is the faster path to the answer.
• Automation, Acceleration, and Reproducibility
• Build automated simulation pipelines, high-throughput screening frameworks, and parametric studies under version control, such that results can be reproduced by someone other than their author.
• Apply machine learning — surrogate models, property prediction, active learning — to reduce simulation burden and widen design-space coverage.
• Build reusable model libraries and standardized protocols so that methods outlive individual projects.
• Validation and Model Credibility
• Define the experimental characterization needed to parameterize and validate models — for example DLS, SAXS/SANS, bulk and interfacial rheology, calorimetry, and NMR — in partnership with internal groups and external collaborators.
• Document assumptions, verification, validation, and uncertainty at a level matched to the risk of the decision the model informs, so that molecular-scale evidence carries documented credibility when the decisions it informs are later defended internally or to regulators.
• Collaboration, Coordination, and Communication
• Serve as the DDCS technical point of contact for Lilly's computational chemistry and cheminformatics communities; define clear capability boundaries and reuse enterprise methods, tools, and licenses rather than rebuilding them.
• Explain molecular-scale results, assumptions, and limits to non-specialists — formulation scientists, device engineers, and program leadership — in terms of the decision in front of them.
• Author and review technical reports; publish and present externally where it strengthens Lilly's capability, network, and ability to recruit.

Basic Requirements:

• Earned Master's degree in computational chemistry, chemical physics, chemical engineering, materials science, biophysics, physics, or a related field, with doctoral or postdoctoral research centered on molecular simulation, with 4+ years of relevant molecular modeling experience
• Demonstrated depth in classical molecular dynamics and coarse-grained/mesoscale simulation, with working proficiency in electronic-structure or semi-empirical methods to derive and validate force-field parameters.
• Hands-on experience with at least one established simulation engine (e.g., GROMACS, LAMMPS, NAMD, OpenMM, HOOMD-blue) and/or quantum chemistry package (e.g., ORCA, Psi4, Gaussian, Q-Chem, CP2K, VASP).
• Strong foundation in statistical mechanics and thermodynamics, and the judgment to assess whether a given force field or level of theory is valid for the system, concentration, and conditions in question.
• Proficiency in Python for simulation setup, trajectory analysis, and post-processing; experience running parallel and/or GPU-accelerated jobs in an HPC environment with a scheduler such as Slurm.
• Working use of version control (Git) and reproducible computational workflows.

Additional Preferences:

• Earned PhD in computational chemistry, chemical physics, chemical engineering, materials science, biophysics, physics, or a related field, with doctoral or postdoctoral research centered on molecular simulation
• Coarse-grained and mesoscale methods: MARTINI or systematic coarse-graining (force matching, relative-entropy minimization, iterative Boltzmann inversion), and dissipative particle dynamics.
• Force-field parameterization workflows for drug-like and ex